Artificial Intelligence is reshaping America's \$9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human-AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approx \$211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approx \$1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how these capabilities may spread under scenarios, Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize investments, and test interventions before committing billions to implementation

翻译：人工智能正在重塑美国9.4万亿美元的劳动力市场，其连锁效应远超出可见的技术领域。当人工智能改造汽车工厂的质量控制任务时，其影响会蔓延至物流网络、供应链和地方服务经济。然而传统劳动力指标无法捕捉这些涟漪效应：它们衡量的是颠覆发生后的就业结果，而非人工智能能力在采用固化前与人类技能的重叠区域。冰山项目通过大型人口模型填补这一空白，模拟人机劳动力市场，将1.51亿劳动者表示为执行超过3.2万种技能、与数千种AI工具交互的自主智能体。该项目提出冰山指数——一种以技能为中心的度量指标，用于衡量人工智能系统在各职业内可执行技能的工资价值。该指数捕捉的是技术暴露（即AI可执行职业任务的能力），而非替代结果或采用时间线。分析表明，集中于计算与技术领域的可见AI采用（占工资价值2.2%，约2110亿美元）仅是冰山一角。技术能力通过认知自动化深入行政、金融和专业服务领域（占11.7%，约1.2万亿美元），其影响规模扩大五倍且地理分布遍及全美各州，而非局限于沿海枢纽。传统指标如GDP、收入和失业率仅能解释不足5%的这种基于技能的变异，凸显了需要新指数来捕捉AI经济中暴露程度的必要性。通过模拟这些能力在不同情境下的扩散路径，冰山项目使政策制定者和企业领袖能够在投入数十亿美元实施前，识别暴露热点、优先投资方向并测试干预措施。